We present experiments on two recommendation tasks, following the protocols of [1]}, [2]}. First, we address music recommendation with Lastfm-2k from [3]} which contains real listening counts of \(2k\) 
users for \(19k\)  artists on the online music service Last.fmhttps://www.last.fm/. We filter the \(2,500\)  items having the most listeners. In order to show how the algorithm scales, we also consider the MovieLens-20m dataset [4]}, which contains ratings in \([0.5,5]\)  of movies by users, and we select the top \(15,000\)  users and items with the most interactions.
